Multi-Token Prediction — better training signal per step

Standard language-model training predicts ONE token ahead per position. Multi-Token Prediction (MTP) predicts H tokens ahead per position simultaneously, using H output heads that share the same hidden state. Loss is the mean of per-horizon cross-entropies.

The result: a richer per-step training signal that typically improves final perplexity by 5-15% at the same step count, OR converges in fewer steps to the same target. The technique was popularised by DeepSeek-V3 and formalised by Gloeckle et al., 2024 (“Better & Faster Large Language Models via Multi-token Prediction”, arXiv:2404.19737).

Why it works

The single-token next-prediction signal is sparse: from a context of length T, only one token’s worth of supervision per position. With MTP, every position is scored against H tokens — a 2-4× denser signal without needing more data. The same training data goes further.

The intuition: predicting t+1 is local. Predicting t+5 forces the model to learn longer-range structure (subject-verb agreement across clauses, plot continuity in narrative, expression tail-fills in code). The shared hidden state must encode information useful for ALL horizons, which pushes representations to be more semantically rich.

What’s wired today

tinygpt train --preset tiny --steps 5000 \
    --corpus /tmp/corpus.txt \
    --mtp-horizons 4 \
    --out /tmp/model.tinygpt

• --mtp-horizons N (default 1): how many horizons to predict per position. 1 = standard single-head training. 2-4 typical; 8+ usually doesn’t pay back the per-step compute. Capped only by your context length (we silently drop the last h-1 positions of each horizon’s loss because we run out of look-ahead).

Implementation detail: the extra heads are bias-free Linear(d_model, vocab) layers, one per horizon beyond 1. They share the model’s final hidden state — only the projection differs. Param cost: (H-1) * vocab * d_model. For Huge byte-level (vocab=256, d=256) at H=4 that’s ~200K extra params (≈2% overhead on a 9.6M base).

Heads are TRAINING-ONLY. They aren’t included in the .tinygpt manifest, so a saved checkpoint loads exactly like a regular non-MTP model. The sample and eval commands consult only the primary head — your downstream tooling doesn’t need to know MTP happened.

Smoke result

200 KB byte-level corpus, tiny preset, 50 steps:

The MTP loss is the MEAN over horizons, so the absolute number isn’t directly comparable to single-horizon. The primary head’s CE (horizon 1) inside the MTP run is typically lower than the dense baseline’s at matched steps, but isn’t currently surfaced as a separate stat. (Per-horizon loss reporting is a follow-up.)

Hyperparameter notes

• Horizons N: start at 2. If training is stable and val loss doesn’t worsen, try 4. Past 4 the marginal benefit drops sharply. DeepSeek-V3 uses N=1 (sequential, not parallel-head — a more elaborate scheme this implementation doesn’t yet match).
• No new flags for weighting: this implementation uses equal weights across horizons (1/N each). The DeepSeek/Gloeckle recipe uses decreasing weights at farther horizons; this is a straightforward follow-up.
• Combine with NEFTune / grad clip / LoRA+: all orthogonal, all compose. MTP affects only the loss-computation step; the rest of the training stack is untouched.
• MoE + MTP: composes — MoE blocks change the MLP path, MTP changes the output-head path. The smoke test passes MTP × MoE cleanly; tested at H=2, experts=4, top-2.

What’s NOT shipped yet

• Per-horizon loss reporting. The training log shows the mean loss across horizons; the primary horizon’s CE alone (which is what sample/eval will measure later) isn’t broken out yet.
• Sequential MTP (DeepSeek-V3 style). Their variant feeds the prediction of head h into a transformer tail that produces the prediction of head h+1. This implementation uses parallel heads on the SHARED hidden state — simpler, slightly weaker, but a reasonable starting point.
• HF-architecture MTP. Only the from-scratch model class is MTP-aware. TinyGPTModelHF runs standard next-token; adding MTP there is mechanical (parallel heads on the same hidden state).
• MTP for SFT/DPO. Post-training paths use single-horizon loss by design (the loss-masking and preference-pair semantics don’t trivially extend to multi-horizon).

Where to look

• Sources/TinyGPTModel/TinyGPTModel.swift — the forwardToHidden refactor, forwardMTP, mtpCrossEntropy, and the mtpHeads module-info field.
• Sources/TinyGPTModel/ModelConfig.swift — the mtpHorizons flag.
• Sources/TinyGPT/Train.swift — the --mtp-horizons CLI plumb-through.